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对基于机器学习的方法进行全面的审查和评估,以识别瘤T细胞抗原

Watshara Shoombuatong¹, Saeed Ahmed², Sm Hasan Mahmud³

¹Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Computational biology and chemistry

|April 11, 2025

概括

计算方法,特别是机器学习,加速瘤T细胞抗原 (TTCA) 的识别. 本综述调查了现有的TTCA预测器,分析了它们的方法和性能,以指导未来的高精度TTCA发现.

科学领域:

免疫学免疫学免疫学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

准确识别瘤T细胞抗原 (TTCAs) 对癌症免疫治疗至关重要.
实验性TTCA识别是繁的;计算预测提供了一个更快的替代方案.
机器学习 (ML) 方法越来越多地用于in silico TTCA预测.

研究的目的:

为TTCA识别提供传统的ML和集体学习方法的首次全面审查.
分析和比较现有的TTCA预测器的有效性和稳定性.
为开发新的计算TTCA识别方法提供见解和建议.

主要方法:

关于最先进的TTCA预测器的综合文献调查.
检查核心算法,方法,数据集,特征编码和特征选择.
在基准数据集和案例研究上分析和比较预测器性能.

主要成果:

详细检查各种TTCA预测器发展方面.
对现有的预测器有效性和稳定性的比较分析.
目前TTCA预测工具的优缺点概述.

结论:

本综述为TTCA识别领域的研究人员提供了有价值的指导.
获得的洞察力将有助于开发更准确的计算TTCA预测器.
改进的TTCA预测器将增强癌症免疫治疗的抗原鉴定.

关键词:

生物信息学是一种生物信息学.机器学习机器学习绩效基准测试绩效基准测试绩效基准测试绩效基准测试序列分析是指进行序列分析.这是一种T细胞抗原.

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